Prakash, M and Murty, Narasimha M (1997) Hebbian learning subspace method: A new approach. In: Pattern Recognition, 30 (1). pp. 141-149.
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In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). It uses the notion of a weighted squared orthogonal projection distance winch gives different weightages to different basis vectors in the computation of the orthogonal projection distance. The principle applied during learning is the same as that used in the earlier Learning Subspace Method (LSM): the projection on the wrong subspace is always decreased and the one on the correct subspace is always increased. We also propose a neural implementation for the HLSM. Experiments have been conducted on an extensive numeric set of handprinted characters involving 16659 samples using the SPRM, the HLSM and the Averaged LSM. Excellent results have been obtained using all the subspace methods thus demonstrating the suitability of subspace methods for this application.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier.|
|Keywords:||Subspace methods;Learning methods;Optical character recognition;Neural networks;Weighted distance|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||23 Feb 2007|
|Last Modified:||19 Sep 2010 04:35|
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